ALS Research: Q&A with Marc Weisskopf

Marc Weisskopf, an associate professor of environmental and occupational epidemiology at Harvard School of Public Health in Boston, has an MDA grant to study the relationship between race and ethnicity, education, specific occupations and the risk of developing ALS.

Article Highlights:

Only a small portion of ALS can be explained with genetics, which leaves open the possibility that nongenetic factors play a role in the disease.

Although epidemiological research in ALS lags behind that of other diseases, new techniques are helping scientists make headway.

Weisskopf — an associate professor of environmental and occupational epidemiology at Harvard School of Public Health in Boston — is studying the relationship between race and ethnicity, education, specific occupations and the risk of developing ALS. (Epidemiology is the study of the patterns, causes and effects of human health and disease.)

“The search for nongenetic risk factors for ALS has progressed slowly over the past decades,” Weisskopf explains. “Although numerous hypotheses have been proposed, outside of age and male gender, no risk factor has emerged as a consistent and accepted predictor of risk.” In a November 2012 discussion about his work, Weisskopf described the current state of epidemiological research in ALS.

Q: How do genetic and nongenetic factors work together to cause disease?

MDA grantee Marc Weisskopf

A: Ultimately, there’s this blending of genes and environment. Genes carry the code that's used in building the components of the nervous system such as [structural] proteins and enzymes. A change in a particular gene might reduce the effectiveness of an enzyme and therefore predispose someone to ALS. Then again, that may only happen if you have the right environmental exposures.

The classic example of this is PKU [phenylketonuria, a rare condition in which a baby is born without the ability to properly break down the amino acid phenylalanine]. PKU is caused by a genetic mutation, so people say PKU is 100 percent a genetic disease. It is — but it also is 100 percent environmental. If you don’t have a PKU mutation, you’re not going to get PKU problems. If you do have a PKU mutation but you never are exposed to phenylalanine, you also are never going to have problems. You have to have the PKU mutation and exposure to phenylalanine to have the problem. That interaction of genes and environment, albeit not likely in such a stark way, is probably what’s going on with a lot of diseases — ALS included.

Q: What evidence, or what kind of evidence, suggests to you and others that nongenetic factors play a role in ALS?

A: We are only able to explain a small portion of ALS with genetics. That is, much of the disease remains unexplained by genetics or other factors. So, there’s a lot of room for possible nongenetic factors, and it makes scientists want to pursue those types of risk factors as possibilities.

There are specific suggestions that things in the environment might be playing a role. For example, people often point to the epidemic levels of ALS that occurred in Guam and at a couple other sites in the South Pacific [from around 1945 to 1960]. Those rates decreased very dramatically over the course of a few decades. A lot of work went on in those areas and there’s clearly some genetic component there, but the kind of decrease in rates of the disease that were seen over such a short period of time isn’t consistent with a purely genetic disease. There must be something else playing a role, and the rapid decline over time sugests that it's something in the environment.

Then there’s also a fair amount of research that has gone into the search for nongenetic risk factors for human disease. ALS lags behind other neurological diseases in terms of epidemiological research, but there are certainly suggestions from various studies that have come out that some environmental factors may be playing a role.

Q: In describing your current MDA-funded study, you note that age and male gender are accepted nongenetic factors. Isn't male gender considered a genetic factor?

A: Certainly it could involve a genetic component, particularly if it's specific to the Y chromosome. But I mean it's nongenetic in the sense that we're not talking about a specific gene. Higher rates of ALS among males could be related to environmental exposures that males are more likely to encounter than women.

Q: Could male gender be a marker for something else in ALS, such as a tendency toward engaging in more physical labor?

A: Yes, exactly. That's what we generally think — it's not just the fact of being male. Genetics may play some role. For example, on the one hand, there may be slight genetic differences that lead to differences in [the male hormone] testosterone that could have an impact. On the other hand, it could be that environmental contaminants exist that mimic the effects of testosterone and to which men are more often exposed. Such contaminants might raise the risk of ALS in the same way, independent of any genetic differences between people.

Q: Aside from race and ethnicity, education and occupation, what other possible nongenetic risk factors for ALS are you interested in studying?

A: Lead exposure is one possible factor that interests me; formaldehyde exposure is another.

In addition, I'm interested in a possible connection between cigarette smoking and ALS. The issue with smoking is that there is intriguing evidence that it plays a role, but the epidemiological evidence still has some oddities to it that make me less certain, despite some papers that have come out saying that it definitely is a risk factor.

I personally think that there is a lot more research to do to determine what role, if any, cigarette smoking plays — or whether it's maybe just a marker for something else. But there is definitely a very interesting signal with smoking. I just think we need to delve a little further to find the specifics.

Another angle that I’m very interested in is a possible connection between head injury — head "trauma" — and ALS. The tricky part with head trauma is figuring out what exactly the trauma was. The easiest way to do it is to use hospital records, but then you’re dealing with very particular types of trauma — ones that prompted people to go to the hospital in the first place. Other types of head injury — lower-intensity or repeated concussions, for example — aren't represented. So, it's a tricky topic.

Q: What makes this kind of study tricky?

A: Well, for example, I published study results awhile back about the military and ALS. It came out after the publication of a couple other papers on the Gulf War, when people were beginning to suspect a connection between neurotoxic agents used there and their possible connection to ALS. Our data indicated that military service increases one's risk of ALS. But I was looking at the military population as a whole — not the subset of those who served in the Gulf War. There seemed to be something about military service alone, regardless of era, that was relevant.

The question remains, however, what is it about military service that increases ALS risk? Presumably it is not the act of signing up for the military. It could be that people's jobs in the military play a role — or maybe something else about being in the military is connected. I'm looking at everybody in the military and, overall, the risk looks to be elevated. But it's possible that may be driven by a subset of people in the military who have an even higher risk of ALS. I'm very interested in exploring that further and figuring out what is it about being in the military that raises one's risk.

Q: You’re looking for ALS risk factors, but might you also find modifiers of the disease?

A: Yes, it's certainly possible, but at this stage — for the purposes of the MDA grant, at least — we're at a much more basic level. We're really just trying to see if we get any signals at all before we look at things that might modify other exposures.

It may be, for example, that head trauma creates inflammation in the brain and makes it more sensitive to something else that comes along later. These are definitely questions that are worth asking, but from an epidemiological perspective, the first step is to look at the factors themselves and then as we develop a little bit more of a picture of what might be going on, we might get ideas of whether something is a straight risk factor in and of itself, or if it's modifying the effects of something else.

Q: Why are epidemiological studies so susceptible to biases?

A: Because ALS is relatively rare, the way people have most often approached ALS epidemiology studies is to go to a hospital or clinic that specializes in ALS, or find some other way to recruit a group of people with ALS. They ask patients or their family members questions about their pasts. Then they locate many people without ALS, and they ask them the same questions.

It’s a very powerful technique because it gets around the problem of needing huge samples of people. But at the same time it opens up the possibility that aspects of the disease may affect the actual biology you're measuring — for example, someone who learns they have ALS may stop smoking or become less physically active — or the way the study participants answer the questions. This can introduce bias in the results. This study approach also opens up another problem: When you look for people who don’t have the disease, what you really need are people in the general population who are representative of [have the same general characteristics as] the people with ALS. This is because what you’re trying to do with those controls is find out what exposures those people with ALS would have had, had they not developed ALS. If you recruit controls that are different from the population the people with ALS came from, you can get biased results.

Ideally, what we want to do is collect a whole bunch of people, and ask them all sorts of questions about lifestyle, habits and things of that sort. Then we follow them over time and track the kinds of things that they do — like what occupations they're in, whether they smoke, how much they smoke. Then we see who develops ALS and who doesn’t. Collecting this data before people get ALS alleviates some of the problems I mentioned. Even with this approach, however, sometimes in a given population two factors happen to go together to a high degree. One may be an actual cause of ALS, but if we only measure the other, we would identify the wrong factor.

Q: What method are you using in your work?

A: I am using the method of following a large group of people over time with data collected before they get ALS. This is one of the few times this kind of approach has been done, although more people are trying it now. You sort of “piggyback” on very large existing cohorts [groups of people] that were assembled many years ago for something different, and you use it now to track who got ALS. It is only possible to do with ALS if the cohort is very large.

My MDA-funded work takes advantage of the National Longitudinal Mortality Study, a cohort study of almost 2.4 million men and women, chosen to be representative of the U.S. population, who completed the Current Population Survey of the U.S. Bureau of the Census between 1973 and 2002. Out of that, a lot are going to get ALS. But all the data that I’m using to compare people who did and didn’t get ALS were collected before any of them ever developed the disease. [Note: This is known as prospectively collected data.]

Our analytical approach is to examine how the factors determined at baseline relate to subsequent risk of death from ALS. For this we’re using the National Death Index [a database that provides data on the causes of nearly all deaths in the United States since 1979].

We have already identified 713 deaths from ALS, which, even as a minimum of the eventual total we will have, makes this study not only one of the largest of the few cohort studies with prospectively collected data, but also the only one that is representative of the U.S. population.

Q: Why is this work so important?

A: In trying to understand any disease, we essentially want to find out as much about it as possible. There's a public health angle, which is that if you can identify some exposure that puts people at a higher risk for ALS, you can try to get rid of the exposure and possibly prevent the disease.

It also can give you clues as to what is going on in the disease. Ideally, we might then try to parallel with animal work to try to figure out what an identified risk factor is doing with respect to motor neurons.

Q: Where are we with ALS?

A: I would say for ALS the bottom line is, unfortunately, we’re at a very early stage in the epidemiological research into the disease. That’s not to say people haven't been doing it for a very long time. But we haven’t had tremendously strong results yet. It’s kind of early days.

I’m very heartened, though, by the fact that a lot of people are trying to apply the same type of approach we’re using to study ALS. I take that as a very promising sign for the epidemiology of ALS.

I think we can look to a different disease in which the research is a little more advanced, like Parkinson's disease. I work with a colleague who has done a lot of work on caffeine and smoking and their roles in Parkinson's disease. He has a close connection with a laboratory researcher who works on receptor systems that may be implicated in Parkinson's disease and that also seem to be affected by caffeine. They’re using that to sort of tweak the thinking in a laboratory setting about what molecular pathways may be important in the disease. That’s the kind of thing that ideally will happen when there’s this sort of interface or interplay between epidemiology and the laboratory.

In the case of Parkinson's disease, some findings (that I was involved with, too) have led to some clinical trials. That’s a way in which the epidemiology can potentially push the clinical side of things.

Q: What’s there to be excited about?

A: One of the main things that is exciting about this — about my current MDA-funded project and others that are beginning to happen — is the chance to take this approach of following lots of people prospectively and collecting the data before they develop the disease, to try and identify the risk factors. That’s what’s been somewhat lacking in the epidemiology of this field for a while. And I think it’s very exciting that it’s being used now.

We can’t always ask every question we want to ask because we're "piggybacking" on existing cohorts, but that’s just sort of the ebb and flow of research. This new type of approach in the ALS setting, in my mind, is very exciting — and it’s happening more and more.